Axiomatisation of fully probabilistic design

Kárný, Miroslav; Kroupa, Tomáš

doi:10.1016/j.ins.2011.09.018

Cited by 47 publications

(27 citation statements)

References 11 publications

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“…This section describes general solution of servo problem within the FPD. Advantages of the FPD's use in comparison with other approaches are described in [8], here they are omitted to save space.…”

Section: Servo Problem Formulationmentioning

confidence: 99%

Servo Problem within Fuel Consumption Optimization

Suzdaleva¹,

Nagy²,

Pavelková³

et al. 2012

Engineering and Applied Science

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The presented paper deals with a problem of fuel consumption optimization. Today's automotive industry solves this problem mainly via various conceptual approaches (hybrid and electric vehicles). However, it leads to high initial cost of a vehicle. This paper focuses on fuel economy for conventional vehicles. For this aim, recursive algorithms of adaptive optimal quadratic control under Bayesian methodology are used. A stochastic servo problem, including setpoint tracking, is a part of the considered adaptive control design. In this paper, fuel consumption and speed of a driven vehicle are the controlled variables, where the first one is to be optimized and the second one is pushed to track its set-point. This set-point is a recommended roaddependent speed. Experiments with real data measured on a driven vehicle are provided.

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Section: Servo Problem Formulationmentioning

confidence: 99%

Servo Problem within Fuel Consumption Optimization

Suzdaleva¹,

Nagy²,

Pavelková³

et al. 2012

Engineering and Applied Science

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“…For an axiomatic justification of the FPD, and the role of the ideal distribution the reader is referred to [7].…”

Section: Remarkmentioning

confidence: 99%

Generalised Probabilistic Control Design for Uncertain Stochastic Control Systems

Herzallah

2018

Asian Journal of Control

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In this paper a novel generalised fully probabilistic controller design for the minimisation of the Kullback-Leibler divergence between the actual joint probability density function (pdf) of the closed loop control system, and an ideal joint pdf is presented for a linear Gaussian uncertain class of stochastic systems. A single layer neural network is used to approximate the probability density function of the system dynamics. The generalised probabilistic control law is obtained by solving the recurrence equation of dynamic programming to the fully probabilistic design control problem while taking into consideration the dependency of the parameters of the estimated probability density function of the system dynamics on the input values. It is shown to be of the class of cautious type controllers which accurately minimises the value of the Kullback-Leibler divergence without disregarding the variance of the model prediction as an element to be minimised. Comparison of theoretical and numerical results obtained from the F-16 fighter aircraft application with existing state-of-the-art demonstrates the effectiveness of the proposed method.

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“…Its details and axiomatic background are left aside. It suffices to recall that it densely extends the set of control problems that can be formulated and solved within Bayesian framework [13]. Note that a closely related and independently developed technique [17], [18] is referred to as KL control.…”

Section: Adaptive Control Of Single Node Withmentioning

confidence: 99%

“…It recursively estimates parameters of a simple model while coping with inevitable approximation errors via stabilised forgetting [9], [10]. It exploits this model for control design via fully probabilistic design of controllers (FPD, [11], [12], [13]). In FPD, the optimal randomised controller is the minimiser of the Kullback-Leibler divergence [14] of the probability density (pd) describing closed loop dynamics to its ideal counterpart.…”

Section: Introductionmentioning

confidence: 99%

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Scalable Harmonization of Complex Networks With Local Adaptive Controllers

Kárný

Herzallah

2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Computational and communication complexities call for distributed, robust, and adaptive control. This paper proposes a promising way of bottom up design of distributed control in which simple controllers are responsible for individual nodes. The overall behaviour of the network can be achieved by interconnecting such controlled loops in cascade control for example, and by enabling the individual nodes to share information about data with their neighbours without aiming at unattainable global solution. The problem is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, that can be implemented adaptively and which provide a systematic rich way to information sharing. The paper elaborates the overall solution, applies it to linear-Gaussian case and provides simulation results.

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Axiomatisation of fully probabilistic design

Cited by 47 publications

References 11 publications

Servo Problem within Fuel Consumption Optimization

Servo Problem within Fuel Consumption Optimization

Generalised Probabilistic Control Design for Uncertain Stochastic Control Systems

Scalable Harmonization of Complex Networks With Local Adaptive Controllers

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